Stream Data Identifiability Assessment for Real-Time Privacy
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Solution Overview
Problem
Existing anonymization processes, such as k-anonymization, are inadequate for real-time processing of stream data due to the lack of sufficient data volume, which can lead to incomplete anonymization and potential identification of individuals from positional information and other personal data.
Innovation Solution
An information determination apparatus that stores stream data in sequence and determines whether the number of data pieces meets a predetermined threshold before assessing if an individual can be identified, thereby preventing unnecessary anonymization and ensuring timely, secure data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If k-anonymization is applied to stream data, then data privacy is improved, but the process becomes unnecessarily complex and time-consuming when data volume is insufficient
Solution Approach 1:
The system performs preliminary evaluation of data volume and identifiability risk before executing the anonymization process. By checking whether the number of data pieces meets a predetermined threshold and whether an individual can be identified from the data, the system avoids unnecessary anonymization operations, thereby reducing processing complexity and time while maintaining privacy protection where needed.
2Speed
If stream data is processed in real-time, then processing speed is improved, but data volume is insufficient for effective anonymization
Solution Approach 1:
The system applies a threshold-based approach where anonymization is executed only when the data volume reaches a predetermined level. This partial action strategy allows the system to maintain real-time processing capability by skipping unnecessary anonymization operations when data volume is insufficient, while still providing comprehensive protection when adequate data is available.
Solution Approach 2:
Before performing anonymization, the system preliminarily checks whether the data volume meets the required threshold. This preliminary evaluation enables the system to make informed decisions about whether to proceed with anonymization, balancing real-time processing requirements with the need for sufficient data volume for effective privacy protection.
3Reliability
If anonymization is performed on insufficient data, then privacy protection is attempted, but the anonymization becomes incomplete and ineffective
Solution Approach 1:
The system performs preliminary checks on data volume and identifiability risk before executing anonymization. By evaluating whether the number of data pieces meets a predetermined threshold and whether an individual can be identified from the data, the system ensures that anonymization is only performed when the data is sufficient, thereby maintaining both privacy protection and anonymization completeness.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor data volume and identifiability risk. Based on this feedback, the system dynamically decides whether to execute anonymization, ensuring that privacy protection is only applied when the data volume and risk assessment justify the operation, thereby avoiding incomplete or ineffective anonymization.
Data Source
AI summary
An information determination apparatus includes a first storage unit configured to store stream data pieces obtained in time sequence; a first determining unit configured to determine whether the number of stream data pieces stored in the first storage unit is at least equal to a predetermined value; and a second determining unit configured to determine, when the number of the stream data pieces stored in the first storage unit is equal to or greater than the predetermined value, whether an individual can be identified based on a dataset composed of a plurality of the stream data pieces stored in the first storage unit, and output the dataset used for the determination and the determination result.